331 lines
8.5 KiB
Markdown
331 lines
8.5 KiB
Markdown
# Checkpoint 2: Historical Context Report
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## Analysis of YouTube Summarizer Evolution & Lessons Learned
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### 1. Media Processing Evolution
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#### ✅ Successful Patterns
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**Download-First Architecture**
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- Always download media before processing (aligns with requirements)
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- Prevents streaming failures and network issues
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- Enables retry without re-downloading
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- Allows offline processing
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**Format Agnostic Processing**
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- Handled MP3, MP4, WAV through FFmpeg conversion
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- Standardized to 16kHz mono WAV internally
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- Reduced processing complexity
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**Staged Pipeline**
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- Clear stages: Download → Convert → Transcribe → Process → Export
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- Each stage independently testable
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- Failure isolation between stages
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**M3 Optimization Success**
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- 20-70x speed improvement with distil-large-v3
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- Smart chunking for memory management
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- Audio preprocessing gave 3x performance boost alone
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#### ❌ Failed Approaches
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**YouTube API Dependency**
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- Rate limits caused reliability issues
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- API availability problems
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- Better to download and process locally
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**Direct Streaming Transcription**
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- Network interruptions caused failures
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- Couldn't retry without full re-download
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- Much slower than local processing
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**Multiple Transcript Sources**
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- Tried to merge YouTube captions with Whisper
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- Added complexity without quality improvement
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- Single source (Whisper) proved more reliable
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**Metadata Preservation Attempts**
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- Tried to maintain all YouTube metadata
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- Most metadata wasn't useful
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- Focus on content over metadata
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### 2. AI Agent Patterns for Code Generation
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#### ✅ What Worked for Consistency
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**DATABASE_MODIFICATION_CHECKLIST.md**
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- Forced systematic approach to schema changes
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- Prevented breaking migrations
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- Created reproducible process
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**Registry Pattern**
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- Solved SQLAlchemy "multiple classes" errors
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- Centralized model registration
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- Thread-safe singleton pattern
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**Test-Driven Development**
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- Test runner with intelligent discovery
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- Markers for test categorization
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- 0.2s test discovery time
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**Strict Documentation Limits**
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- 600 LOC limit prevented context drift
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- Forced concise, focused documentation
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- Improved AI agent comprehension
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#### ❌ What Failed
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**Loose Context Management**
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- Led to inconsistent implementations
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- Agents made conflicting decisions
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- No clear source of truth
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**Parallel Development**
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- Frontend/backend simultaneously caused chaos
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- Integration issues multiplied
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- Sequential development proved superior
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**Undefined Rules**
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- Different agents used different patterns
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- No consistency across sessions
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- Architecture drift over time
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**No Approval Gates**
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- Changes happened without oversight
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- Breaking changes introduced silently
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- Lost control of project direction
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### 3. Content Generation Insights
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#### ✅ Structured Output Success
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**Template-Driven Generation**
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- Jinja2 templates ensured consistency
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- Easy to modify output format
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- Separation of logic and presentation
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**Multi-Agent Perspectives**
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- Technical/Business/UX viewpoints valuable
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- But expensive ($0.015 per analysis)
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- Cached results for 7 days
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**JSON-First Approach**
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- Everything stored as structured data
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- Other formats generated from JSON
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- Single source of truth
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**Export Pipeline**
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- JSON → other formats on demand
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- Reduced storage needs
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- Flexible output options
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#### ❌ Content Generation Failures
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**Unstructured Prompts**
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- Led to inconsistent outputs
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- Quality varied between runs
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- Hard to parse results
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**No Validation Schemas**
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- Output structure varied
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- Breaking changes in format
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- Integration failures
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**Missing Context Windows**
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- Lost important information in long transcripts
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- No chunk overlap strategy
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- Discontinuity in output
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**Over-Complex Workflows**
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- Multi-stage enhancement didn't improve quality
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- Simple one-pass enhancement worked better
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- Diminishing returns on complexity
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### 4. Caching Architecture Lessons
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#### Best Decision: Multi-Layer Caching with Different TTLs
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**Why It Worked:**
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- Different data has different lifespans
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- Embeddings stable for 24h
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- Multi-agent results valid for 7d
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- Query results fresh for 6h
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**Cost Impact:**
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- 90% reduction in API calls
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- $0.015 saved per multi-agent analysis
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- 2+ seconds saved per cache hit
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#### Recommendation for Starting Fresh
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**Start with Embedding Cache First** because:
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1. Highest impact (90% API reduction)
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2. Simplest to implement
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3. Benefits all AI operations
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4. Can add other layers incrementally
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### 5. Database Evolution
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#### Journey: SQLite → PostgreSQL (planned) → SQLite (reality)
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**Key Learning**: SQLite was sufficient because:
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- Single instance deployment
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- Built-in with Python
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- No connection overhead
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- Excellent for caching
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- Easy backup/restore
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**PostgreSQL Benefits** (for Trax):
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- Multiple services can connect
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- Better concurrent writes
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- Professional features (JSONB)
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- Cloud deployment ready
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- Better testing tools
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**Recommendation**: Start with PostgreSQL from day one since you're planning multiple services (summarizer, frontend server).
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### 6. Export System Evolution
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#### Original Approach
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- Complex multi-format system
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- PDFs, HTML, Markdown, etc.
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- Template system for each format
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- High maintenance burden
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#### Final Success: JSON + TXT Backup
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**Why This Worked:**
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- JSON = structured, parseable, universal
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- TXT = human-readable, searchable, backup
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- Other formats generated on-demand from JSON
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- Reduced complexity by 80%
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- Storage requirements minimal
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This aligns perfectly with your requirements!
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### 7. Performance Optimization Journey
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#### What Worked
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**Faster Whisper Integration**
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- 20-32x speed improvement over OpenAI Whisper
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- CTranslate2 optimization engine
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- Native MP3 processing without conversion
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**Model Selection**
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- large-v3-turbo: Good balance
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- distil-large-v3: Best for M3 (20-70x improvement)
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- int8 quantization: Great CPU performance
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**Audio Preprocessing**
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- 16kHz conversion: 3x data reduction
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- Mono channel: 2x data reduction
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- VAD: Skip silence automatically
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#### What Failed
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**GPU Optimization Attempts**
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- M3 Metal support inconsistent
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- CPU with int8 actually faster
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- Complexity not worth it
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**Real-Time Processing**
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- Buffering issues
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- Latency problems
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- Batch processing superior
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### 8. Testing Evolution
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#### Failed Approach: Mock Everything
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- Mocked services behaved differently
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- Didn't catch real issues
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- False confidence in tests
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#### Success: Real Files, Real Services
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- Small test files (5s, 30s, 2m)
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- Actual Whisper calls in integration tests
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- Caught real edge cases
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- More reliable results
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### 9. Critical Success Factors Discovered
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#### For AI Code Generation Consistency
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1. **Explicit Rules File**: Like DATABASE_MODIFICATION_CHECKLIST.md
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2. **Approval Gates**: Each major change requires permission
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3. **Test-First**: Write test, then implementation
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4. **Single Responsibility**: One task at a time
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5. **Context Limits**: Keep docs under 600 LOC
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#### For Media Processing Reliability
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1. **Always Download First**: Never stream
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2. **Standardize Early**: Convert to 16kHz mono WAV
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3. **Chunk Large Files**: 10-minute segments with overlap
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4. **Cache Aggressively**: Transcriptions are expensive
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5. **Simple Formats**: JSON + TXT only
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#### For Project Success
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1. **Backend-First**: Get data layer right
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2. **CLI Before GUI**: Test via command line
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3. **Modular Services**: Each service independent
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4. **Progressive Enhancement**: Start simple, add features
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5. **Document Decisions**: Track why choices were made
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### 10. Architectural Patterns to Preserve
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**Database Registry Pattern**
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```python
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# Prevents SQLAlchemy conflicts
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class DatabaseRegistry:
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_instance = None
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_base = None
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_models = {}
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```
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**Protocol-Based Services**
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```python
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# Easy swapping of implementations
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class TranscriptionProtocol(Protocol):
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async def transcribe(self, audio: Path) -> dict:
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pass
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```
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**Multi-Layer Caching**
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```python
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# Different TTLs for different data
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cache_layers = {
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'embedding': 86400, # 24h
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'analysis': 604800, # 7d
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'query': 21600, # 6h
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}
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```
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### Summary of Lessons
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**Technical Wins:**
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- Download-first architecture
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- Protocol-based services
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- Multi-layer caching
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- Real test files
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- JSON + TXT export
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**Process Wins:**
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- Backend-first development
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- Explicit rule files
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- Approval gates
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- Test-driven development
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- Documentation limits
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**Things to Avoid:**
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- Streaming processing
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- Mock-heavy testing
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- Parallel development
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- Complex export formats
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- Loose context management
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These lessons form the foundation for Trax's architecture, ensuring we build on proven patterns while avoiding past mistakes.
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---
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*Generated: 2024*
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*Status: COMPLETE*
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*Next: Architecture Design Report* |